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In vehicle NVH development, vibroacoustic simulations with Finite Element (FE) models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time, the need for multiple evaluations of the same model with different input parameters, e.g., for uncertainty quantification, optimization, or robustness investigations, is also increasing. Therefore, it is crucial to reduce the computational costs in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach.

Simulations using computational techniques play a critical role in helping automotive industry adapt to light weighting and come up with future ready designs for maximizing the performance on cost and speed of manufacturing. As global legislation becomes increasingly stringent, light-weighting is a key element that will enable automotive OEMs to meet the emission goals towards net-zero future. Recent advancement in computational techniques like Topology Optimization have boosted the freedom of designers to reduce weight & cost of the part. This project discusses the development of computational models using physics-based software tools, and to validate the functional requirements to optimize the design using topology optimization techniques. The project also aims to reduce material consumption and manufacturing costs of the part while retaining its original stiffness.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

Air conditioning acoustics have become of paramount importance in electric vehicles, where noise from electromechanical components is no longer masked by the presence of the internal combustion engine. In a car HVAC systems, the coolant compressor is one of the most important sources in terms of vibration and noise generation. The paper, the generated structural noise is studied in detail on a prototype installation, and the noise transmission and propagation mechanisms are analyzed and discussed. Through ”in situ” measurements and virtual point transformation, the rotor unbalance forces and torque acting within the component are identified. The dynamic properties of the rubber mounts, installed between the compressor and its support, are identified thanks to matrix inversion methods. To assess the quality of the proposed procedure, the synthesized sound pressure level is compared with experimental SPL measurements in different operational conditions.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

It is particularly easy to get tunnel vision as a domain expert, and focus only on the improvements one could provide in their area of expertise. To make matters worse, many Original Equipment Manufacturers (OEMs) are silo-ed by domain of expertise, unconsciously promoting this single mindedness in design. Unfortunately, the successful and profitable development of a vehicle is dependent on the delicate balance of performance across many domains, involving multiple physics and departments. Taking for instance the design of a Heating, Ventilation & Air Conditioning (HVAC) system, the device’s primary function is to control the climate system in vehicle cabins, and more importantly to make sure that critical areas on the windshield can be defrosted in cold weather conditions within regulation time. With the advent of electric and autonomous vehicles, further importance is now also placed on the energy efficiency of the HVAC, and its noise.

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro 6 and China 6 standard. Optimization of GPF performance requires a delicate trade-off between fuel economy, engine performance and drivability. This results in a complex lengthy and iterative calibration development process which uses a lot of hardware resources. To improve the calibration process and reduce hardware testing, physics-based modeling of the GPF system is used. A 1-D chemical model supplemented with 3D CFD solver is utilized to evaluate pressure drop and soot burning performance characteristics of the GPF under engine dynamometer test conditions. The chemical kinetics of soot burning for the 1D model is developed using test data obtained from well controlled laboratory environment.

It is essential to include uncertainties in the simulation process in order to perform reliable vibroacoustic predictions in the early design phase. In this contribution, uncertainties are quantified using the generalized Polynomial Chaos (gPC) expansion in combination with a Finite Element (FE) model of a vehicle body in white. It is the objective to particularly investigate the applicability of the gPC method in the industrial context with a high number of uncertain parameters and computationally expensive models. A non-intrusive gPC expansion of first and second order is implemented and the approximation of a stochastic response process is compared to a Latin Hypercube sampling based reference solution with special regard to accuracy and computational efficiency. Furthermore, the method is examined for other input distributions and transferred to another FE model in order to verify the applicability of the gPC method in practical applications.

During the last decades, big steps have been taken towards a realistic simulation of NVH (Noise Vibration Harshness) behavior of vehicles using the Finite Element (FE) method. The quality of these computation models has been substantially increased and the accessible frequency range has been widened. Nevertheless, to perform a reliable prediction of the vehicle vibroacoustic behavior, the consideration of uncertainties is crucial. With this approach there are many challenges on the way to valid and useful simulation models and they can be divided into three areas: the input uncertainties, the propagation of uncertainties through the FE model and finally the statistical output quantities. Each of them must be investigated to choose sufficient methods for a valid and fast prediction of vehicle body vibroacoustics. It can be shown by rough estimation that dimensionality of the corresponding random space for different types of uncertainty is tremendously high.

The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce.

This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

Implementing stringent emission norms and fuel economy requirement in the coming decade will be very challenging to the whole automotive industry. Aerodynamic losses contribute up to 13% to 22 % of overall fuel economy and aerodynamicists will be challenged to have optimum content on the vehicle to reduce this loss. Improving Aerodynamic performance of ground vehicles has already reached its peak and the industry is moving towards active mechanisms to improve performance. Calibrating or simulating these active mechanisms in the wind tunnel or in Computational Fluid Dynamics (CFD) would be very challenging as the model complexity increases. Computationally expensive CFD models are required to predict the transient behaviors of model complexity.

The market for electric vehicles and hybrid electric vehicles is expected to grow in the coming years, which is increasing interest in design optimization of electric motors for automotive applications. Under demanding duty cycles, the moving part within a motor, the rotor, may experience varying stresses induced by centrifugal force, a necessary condition for fatigue. Rotors contain hundreds of electrical steel laminations produced by stamping, which creates a characteristic edge structure comprising rollover, shear and tear zones, plus a burr. Fatigue properties are commonly reported with specimens having polished edges. Since surface condition is known to affect fatigue strength, an experiment was conducted to evaluate the effect of sample preparation on tensile and fatigue behavior of stamped specimens. Tensile properties were unaffected by polishing. In contrast, polishing was shown to increase fatigue strength by approximately 10-20% in the range of 105-107 cycles to failure.

Rapidly enhancing engineering techniques to manufacture components in quick turnaround time have gained importance in recent times. Manufacturing strategies like Additive Manufacturing (AM) are a key enabler for achieving them. Unlike traditional manufacturing techniques like injection molding, casting etc.; AM unites advanced materials, machines, and software which will be critical for the fourth industrial revolution known as Industry 4.0. Successful application of AM involves a specific combination and understanding of these three key elements. In this paper the AM approach used is Fused Deposition Modelling (FDM). Since material costs contribute to 60% of the overall FDM costs, it becomes a necessity to optimize the parts. This paper reports the case studies of 3D-printed Automotive Fixtures which utilize computational methods (CAE), topology optimization and FDM constrains (build directions) to manufacture the part.

Shared Mobility is changing mobility trends of Automotive Industry and its one of the Disruptions. The current vehicle customer usage and life of components are designed majorly for personal vehicle and with factors that comprehend usage of shared vehicles. The usage pattern for customer differ between personal vehicle, shared vehicle & Taxi. In the era of Autonomous and Shared mobility systems, the customer usage and expectation of vehicle condition on each & every ride of vehicle will be a vehicle in good condition on each ride. The vehicle needs systems that will guide or fix the issues on its own, to improve customer satisfaction. We also need a transformation in customer behavior pattern to use shared mobility vehicle as their personal vehicle to improve the life of vehicle hardwares & reduce warranty cost. We will be focusing on Vehicle Closure hardware & mechanisms as that will be the first and major interaction point for customers in vehicle.

Multi-layered, high-density polyethylene (HDPE) fuel tanks are increasingly being used in automobiles due to advantages such as shape flexibility, low weight and corrosion resistance. Though, HDPE fuel tanks are perceived to be safer as compared to metallic tanks, the material properties are influenced by service temperature. At higher temperatures (more than 80oC), plastic fuel tanks can soften, sag and eventually spill out the fuel, while the extreme cold (less than -20°C) can lead to potential cracking problems. Damage may also occur due to accidental drop while handling or due to an impact from a flying shrapnel. This can be catastrophic due to flammability of the fuel. The objective of this work is to characterize and develop a failure model for the plastic fuel tank material to simulate damage and enhance predictive capability of CAE for chassis and safety load cases.

Advance Active Safety Systems play a preventive role in mitigating crashes and accidents by providing warning, additional assistance to the driver and maneuverability of vehicle by itself. Some of the features include forward collision warning system and lane departure warning system activate a warning alert when potentially dangerous situations are detected. These active safety features present in developed markets work with Fusion based algorithm combining Radar, Lidar, Camera, Ultrasonic sensor’s input. Application of these algorithms are Intelligent Cruise Control, Collision avoidance, parking assistance, identify pedestrian etc. The complexity of the algorithm, cost of the control unit and road infrastructure are hindrance to emerging market. The solution presented in this paper is towards camera-based solution, describing the method to determine the predictive path, that is obstacle free space and use the predictive space to navigate or steer.

A virtual 'model' is generally a mathematical surrogate of a physical system and when well correlated, serves as a basis for understanding the physical system in part or in entirety. Drive Quality (DQ) defines a driver's 'experience' of a blend of controlled responses to an applied input. The 'experience' encompasses physical, biological and bio- chemical perception of vehicular motion by the human body. In the automotive domain, many physical modeling tools are used to model the sub-components and its integration at the system level. Physical Modeling requires high domain expertise and is not only time consuming but is also very 'compute-resource' intensive. In the path to achieving 'vDQP (Virtual Drive Quality Prediction)' goal, one of the requirements is to establish 'well-correlated' virtual environments of high fidelity with respect to standard test maneuvers. This helps in advancing many developmental activities from a Analysis, Controls and Calibration standpoint.

A study on the effect of indenting power-law shaped profiles on the flexible structures for investigating the vibration damping characteristics using computational simulation method is discussed. The simulation results are checked to see the impact of such features on the damping behavior of flexible structures responsible for radiating noise when excited with fluctuating loads. Though the conventional remedies for solving Noise and vibration issues generally involves tuning of structure stiffness or damping treatment this paper gives an insight on the idea of manipulation of elastic waves within the flexible structure itself to minimize the cross-reflections of the mechanical waves. The simulation studies mentioned in this paper not only hovers over the effectiveness of such features but also will be helpful for the engineers to look through a different perspective while solving N&V issues using simulation tools.

Vehicle thermal management (VTM) simulations constitute an important step in the early development phase of a vehicle. They help in predicting the temperature profiles of critical components over a drive cycle and identify components which are exceeding temperature design limits. Parts with the highest temperatures in a vehicle with an internal combustion engine are concentrated in the engine bay area. As packaging constraints grow tighter, the components in the engine bay are packed closer together. This makes the thermal protection in the engine bay even more crucial. The fan influences the airflow into the engine bay and plays an important role in deciding flow distribution in this region. This makes modelling of the fan an important aspect of VTM simulations. The challenge associated with modelling the fan is the accurate simulation of the rotation imparted by the fan to the incoming flow. Currently, two modelling approaches are prevalent in the industry.